{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phonograph\n",
      "collect\n",
      "MaterialFB\n",
      "commons\n",
      "osmeditor4android\n",
      "AntennaPod\n",
      "MyExpenses\n",
      "AnkiDroid\n",
      "OpenTracks\n",
      "Scarlet-Notes\n",
      "Markor\n",
      "openlauncher\n",
      "ActivityDiary\n",
      "APhotoManager\n",
      "Omni-Notes\n"
     ]
    }
   ],
   "source": [
    "def analyze_task_costs(app_name, droidagent_result_dir):\n",
    "    usage_row = []\n",
    "    with open(os.path.join(droidagent_result_dir, 'exp_data.json')) as f:\n",
    "        exp_data = json.load(f)\n",
    "\n",
    "    api_usage = exp_data['API_usage']\n",
    "    \n",
    "    usage_row.append({\n",
    "        'app_name': app_name,\n",
    "        'gpt-4-prompt-tokens': api_usage['gpt-4-0613']['prompt_tokens'],\n",
    "        'gpt-4-completion_tokens': api_usage['gpt-4-0613']['completion_tokens'],\n",
    "        'gpt-3.5-16k-prompt-tokens': api_usage['gpt-3.5-turbo-16k-0613']['prompt_tokens'],\n",
    "        'gpt-3.5-16k-completion-tokens': api_usage['gpt-3.5-turbo-16k-0613']['completion_tokens'],\n",
    "    })\n",
    "    if 'gpt-3.5-turbo-0613' in api_usage:\n",
    "        usage_row[-1]['gpt-3.5-prompt-tokens'] = api_usage['gpt-3.5-turbo-0613']['prompt_tokens']\n",
    "        usage_row[-1]['gpt-3.5-completion-tokens'] = api_usage['gpt-3.5-turbo-0613']['completion_tokens']\n",
    "    else:\n",
    "        usage_row[-1]['gpt-3.5-prompt-tokens'] = 0\n",
    "        usage_row[-1]['gpt-3.5-completion-tokens'] = 0\n",
    "\n",
    "    return usage_row\n",
    "\n",
    "usage_rows = []\n",
    "for app_name in os.listdir('../data/'):\n",
    "    if app_name == \"QuickChat\":\n",
    "        continue\n",
    "    if app_name == '.keep':\n",
    "        continue\n",
    "    print(app_name)\n",
    "    result_path = os.path.join('../data/', app_name)\n",
    "    task_row = analyze_task_costs(app_name, result_path)\n",
    "    usage_rows.extend(task_row)\n",
    "\n",
    "usage_df = pd.DataFrame(usage_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_rate_map = {\n",
    "    'gpt-4-8k': {\n",
    "        'input': 0.03,\n",
    "        'output': 0.06\n",
    "    },\n",
    "    'gpt-3.5-16k': {\n",
    "        'input': 0.003,\n",
    "        'output': 0.004\n",
    "    },\n",
    "    'gpt-3.5-4k': {\n",
    "        'input': 0.0015,\n",
    "        'output': 0.002\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "usage_df['gpt-3.5-cost'] = (usage_df['gpt-3.5-prompt-tokens'] * cost_rate_map['gpt-3.5-4k']['input'] + usage_df['gpt-3.5-completion-tokens'] * cost_rate_map['gpt-3.5-4k']['output'] + usage_df['gpt-3.5-16k-prompt-tokens'] * cost_rate_map['gpt-3.5-16k']['input'] + usage_df['gpt-3.5-16k-completion-tokens'] * cost_rate_map['gpt-3.5-16k']['output']) / 1000\n",
    "usage_df['gpt-4-cost'] = (usage_df['gpt-4-prompt-tokens'] * cost_rate_map['gpt-4-8k']['input'] + usage_df['gpt-4-completion_tokens'] * cost_rate_map['gpt-4-8k']['output']) / 1000\n",
    "usage_df['total-cost'] = usage_df['gpt-3.5-cost'] + usage_df['gpt-4-cost']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_name</th>\n",
       "      <th>gpt-4-prompt-tokens</th>\n",
       "      <th>gpt-4-completion_tokens</th>\n",
       "      <th>gpt-3.5-16k-prompt-tokens</th>\n",
       "      <th>gpt-3.5-16k-completion-tokens</th>\n",
       "      <th>gpt-3.5-prompt-tokens</th>\n",
       "      <th>gpt-3.5-completion-tokens</th>\n",
       "      <th>gpt-3.5-cost</th>\n",
       "      <th>gpt-4-cost</th>\n",
       "      <th>total-cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>304392</td>\n",
       "      <td>21174</td>\n",
       "      <td>1748985</td>\n",
       "      <td>79749</td>\n",
       "      <td>439807</td>\n",
       "      <td>39894</td>\n",
       "      <td>6.305449</td>\n",
       "      <td>10.40220</td>\n",
       "      <td>16.707650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>collect</td>\n",
       "      <td>406123</td>\n",
       "      <td>30989</td>\n",
       "      <td>2438300</td>\n",
       "      <td>109038</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.751052</td>\n",
       "      <td>14.04303</td>\n",
       "      <td>21.794082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MaterialFB</td>\n",
       "      <td>332063</td>\n",
       "      <td>17622</td>\n",
       "      <td>2202179</td>\n",
       "      <td>74574</td>\n",
       "      <td>676738</td>\n",
       "      <td>51515</td>\n",
       "      <td>8.022970</td>\n",
       "      <td>11.01921</td>\n",
       "      <td>19.042180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>commons</td>\n",
       "      <td>283515</td>\n",
       "      <td>19354</td>\n",
       "      <td>1640214</td>\n",
       "      <td>73902</td>\n",
       "      <td>306695</td>\n",
       "      <td>28227</td>\n",
       "      <td>5.732747</td>\n",
       "      <td>9.66669</td>\n",
       "      <td>15.399437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>osmeditor4android</td>\n",
       "      <td>389953</td>\n",
       "      <td>24513</td>\n",
       "      <td>2255038</td>\n",
       "      <td>93023</td>\n",
       "      <td>508371</td>\n",
       "      <td>43298</td>\n",
       "      <td>7.986359</td>\n",
       "      <td>13.16937</td>\n",
       "      <td>21.155729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AntennaPod</td>\n",
       "      <td>365024</td>\n",
       "      <td>23713</td>\n",
       "      <td>2189736</td>\n",
       "      <td>72587</td>\n",
       "      <td>677486</td>\n",
       "      <td>56478</td>\n",
       "      <td>7.988741</td>\n",
       "      <td>12.37350</td>\n",
       "      <td>20.362241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MyExpenses</td>\n",
       "      <td>350125</td>\n",
       "      <td>22732</td>\n",
       "      <td>2058254</td>\n",
       "      <td>90010</td>\n",
       "      <td>376364</td>\n",
       "      <td>32351</td>\n",
       "      <td>7.164050</td>\n",
       "      <td>11.86767</td>\n",
       "      <td>19.031720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>393146</td>\n",
       "      <td>25480</td>\n",
       "      <td>2292469</td>\n",
       "      <td>106514</td>\n",
       "      <td>545532</td>\n",
       "      <td>47348</td>\n",
       "      <td>8.216457</td>\n",
       "      <td>13.32318</td>\n",
       "      <td>21.539637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OpenTracks</td>\n",
       "      <td>322581</td>\n",
       "      <td>22125</td>\n",
       "      <td>1732418</td>\n",
       "      <td>81869</td>\n",
       "      <td>447297</td>\n",
       "      <td>37829</td>\n",
       "      <td>6.271333</td>\n",
       "      <td>11.00493</td>\n",
       "      <td>17.276263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scarlet-Notes</td>\n",
       "      <td>216113</td>\n",
       "      <td>16578</td>\n",
       "      <td>1417558</td>\n",
       "      <td>66410</td>\n",
       "      <td>598966</td>\n",
       "      <td>45795</td>\n",
       "      <td>5.508353</td>\n",
       "      <td>7.47807</td>\n",
       "      <td>12.986423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Markor</td>\n",
       "      <td>277188</td>\n",
       "      <td>18103</td>\n",
       "      <td>1672009</td>\n",
       "      <td>71858</td>\n",
       "      <td>479117</td>\n",
       "      <td>42043</td>\n",
       "      <td>6.106221</td>\n",
       "      <td>9.40182</td>\n",
       "      <td>15.508040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>openlauncher</td>\n",
       "      <td>253885</td>\n",
       "      <td>19150</td>\n",
       "      <td>1602195</td>\n",
       "      <td>83396</td>\n",
       "      <td>458959</td>\n",
       "      <td>43331</td>\n",
       "      <td>5.915269</td>\n",
       "      <td>8.76555</td>\n",
       "      <td>14.680819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>329085</td>\n",
       "      <td>21778</td>\n",
       "      <td>1938697</td>\n",
       "      <td>82593</td>\n",
       "      <td>686243</td>\n",
       "      <td>57270</td>\n",
       "      <td>7.290368</td>\n",
       "      <td>11.17923</td>\n",
       "      <td>18.469598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>APhotoManager</td>\n",
       "      <td>318981</td>\n",
       "      <td>25115</td>\n",
       "      <td>1863185</td>\n",
       "      <td>92835</td>\n",
       "      <td>599593</td>\n",
       "      <td>42635</td>\n",
       "      <td>6.945555</td>\n",
       "      <td>11.07633</td>\n",
       "      <td>18.021884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>336191</td>\n",
       "      <td>23544</td>\n",
       "      <td>2288514</td>\n",
       "      <td>78479</td>\n",
       "      <td>697017</td>\n",
       "      <td>49192</td>\n",
       "      <td>8.323367</td>\n",
       "      <td>11.49837</td>\n",
       "      <td>19.821737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             app_name  gpt-4-prompt-tokens  gpt-4-completion_tokens  \\\n",
       "0          Phonograph               304392                    21174   \n",
       "1             collect               406123                    30989   \n",
       "2          MaterialFB               332063                    17622   \n",
       "3             commons               283515                    19354   \n",
       "4   osmeditor4android               389953                    24513   \n",
       "5          AntennaPod               365024                    23713   \n",
       "6          MyExpenses               350125                    22732   \n",
       "7           AnkiDroid               393146                    25480   \n",
       "8          OpenTracks               322581                    22125   \n",
       "9       Scarlet-Notes               216113                    16578   \n",
       "10             Markor               277188                    18103   \n",
       "11       openlauncher               253885                    19150   \n",
       "12      ActivityDiary               329085                    21778   \n",
       "13      APhotoManager               318981                    25115   \n",
       "14         Omni-Notes               336191                    23544   \n",
       "\n",
       "    gpt-3.5-16k-prompt-tokens  gpt-3.5-16k-completion-tokens  \\\n",
       "0                     1748985                          79749   \n",
       "1                     2438300                         109038   \n",
       "2                     2202179                          74574   \n",
       "3                     1640214                          73902   \n",
       "4                     2255038                          93023   \n",
       "5                     2189736                          72587   \n",
       "6                     2058254                          90010   \n",
       "7                     2292469                         106514   \n",
       "8                     1732418                          81869   \n",
       "9                     1417558                          66410   \n",
       "10                    1672009                          71858   \n",
       "11                    1602195                          83396   \n",
       "12                    1938697                          82593   \n",
       "13                    1863185                          92835   \n",
       "14                    2288514                          78479   \n",
       "\n",
       "    gpt-3.5-prompt-tokens  gpt-3.5-completion-tokens  gpt-3.5-cost  \\\n",
       "0                  439807                      39894      6.305449   \n",
       "1                       0                          0      7.751052   \n",
       "2                  676738                      51515      8.022970   \n",
       "3                  306695                      28227      5.732747   \n",
       "4                  508371                      43298      7.986359   \n",
       "5                  677486                      56478      7.988741   \n",
       "6                  376364                      32351      7.164050   \n",
       "7                  545532                      47348      8.216457   \n",
       "8                  447297                      37829      6.271333   \n",
       "9                  598966                      45795      5.508353   \n",
       "10                 479117                      42043      6.106221   \n",
       "11                 458959                      43331      5.915269   \n",
       "12                 686243                      57270      7.290368   \n",
       "13                 599593                      42635      6.945555   \n",
       "14                 697017                      49192      8.323367   \n",
       "\n",
       "    gpt-4-cost  total-cost  \n",
       "0     10.40220   16.707650  \n",
       "1     14.04303   21.794082  \n",
       "2     11.01921   19.042180  \n",
       "3      9.66669   15.399437  \n",
       "4     13.16937   21.155729  \n",
       "5     12.37350   20.362241  \n",
       "6     11.86767   19.031720  \n",
       "7     13.32318   21.539637  \n",
       "8     11.00493   17.276263  \n",
       "9      7.47807   12.986423  \n",
       "10     9.40182   15.508040  \n",
       "11     8.76555   14.680819  \n",
       "12    11.17923   18.469598  \n",
       "13    11.07633   18.021884  \n",
       "14    11.49837   19.821737  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gpt-4-prompt-tokens</th>\n",
       "      <th>gpt-4-completion_tokens</th>\n",
       "      <th>gpt-3.5-16k-prompt-tokens</th>\n",
       "      <th>gpt-3.5-16k-completion-tokens</th>\n",
       "      <th>gpt-3.5-prompt-tokens</th>\n",
       "      <th>gpt-3.5-completion-tokens</th>\n",
       "      <th>gpt-3.5-cost</th>\n",
       "      <th>gpt-4-cost</th>\n",
       "      <th>total-cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>325224.333333</td>\n",
       "      <td>22131.333333</td>\n",
       "      <td>1.955983e+06</td>\n",
       "      <td>83789.133333</td>\n",
       "      <td>499879.00000</td>\n",
       "      <td>41147.066667</td>\n",
       "      <td>7.035219</td>\n",
       "      <td>11.084610</td>\n",
       "      <td>18.119829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52952.429998</td>\n",
       "      <td>3723.946594</td>\n",
       "      <td>3.126767e+05</td>\n",
       "      <td>12427.091671</td>\n",
       "      <td>183082.68163</td>\n",
       "      <td>13860.604035</td>\n",
       "      <td>0.989498</td>\n",
       "      <td>1.778203</td>\n",
       "      <td>2.662672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>216113.000000</td>\n",
       "      <td>16578.000000</td>\n",
       "      <td>1.417558e+06</td>\n",
       "      <td>66410.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.508353</td>\n",
       "      <td>7.478070</td>\n",
       "      <td>12.986423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>293953.500000</td>\n",
       "      <td>19252.000000</td>\n",
       "      <td>1.702214e+06</td>\n",
       "      <td>74238.000000</td>\n",
       "      <td>443552.00000</td>\n",
       "      <td>38861.500000</td>\n",
       "      <td>6.188777</td>\n",
       "      <td>10.034445</td>\n",
       "      <td>16.107845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>329085.000000</td>\n",
       "      <td>22125.000000</td>\n",
       "      <td>1.938697e+06</td>\n",
       "      <td>81869.000000</td>\n",
       "      <td>508371.00000</td>\n",
       "      <td>43298.000000</td>\n",
       "      <td>7.164050</td>\n",
       "      <td>11.076330</td>\n",
       "      <td>18.469598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>357574.500000</td>\n",
       "      <td>24113.000000</td>\n",
       "      <td>2.228608e+06</td>\n",
       "      <td>91422.500000</td>\n",
       "      <td>638165.50000</td>\n",
       "      <td>48270.000000</td>\n",
       "      <td>7.987550</td>\n",
       "      <td>12.120585</td>\n",
       "      <td>20.091989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>406123.000000</td>\n",
       "      <td>30989.000000</td>\n",
       "      <td>2.438300e+06</td>\n",
       "      <td>109038.000000</td>\n",
       "      <td>697017.00000</td>\n",
       "      <td>57270.000000</td>\n",
       "      <td>8.323367</td>\n",
       "      <td>14.043030</td>\n",
       "      <td>21.794082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gpt-4-prompt-tokens  gpt-4-completion_tokens  \\\n",
       "count            15.000000                15.000000   \n",
       "mean         325224.333333             22131.333333   \n",
       "std           52952.429998              3723.946594   \n",
       "min          216113.000000             16578.000000   \n",
       "25%          293953.500000             19252.000000   \n",
       "50%          329085.000000             22125.000000   \n",
       "75%          357574.500000             24113.000000   \n",
       "max          406123.000000             30989.000000   \n",
       "\n",
       "       gpt-3.5-16k-prompt-tokens  gpt-3.5-16k-completion-tokens  \\\n",
       "count               1.500000e+01                      15.000000   \n",
       "mean                1.955983e+06                   83789.133333   \n",
       "std                 3.126767e+05                   12427.091671   \n",
       "min                 1.417558e+06                   66410.000000   \n",
       "25%                 1.702214e+06                   74238.000000   \n",
       "50%                 1.938697e+06                   81869.000000   \n",
       "75%                 2.228608e+06                   91422.500000   \n",
       "max                 2.438300e+06                  109038.000000   \n",
       "\n",
       "       gpt-3.5-prompt-tokens  gpt-3.5-completion-tokens  gpt-3.5-cost  \\\n",
       "count               15.00000                  15.000000     15.000000   \n",
       "mean            499879.00000               41147.066667      7.035219   \n",
       "std             183082.68163               13860.604035      0.989498   \n",
       "min                  0.00000                   0.000000      5.508353   \n",
       "25%             443552.00000               38861.500000      6.188777   \n",
       "50%             508371.00000               43298.000000      7.164050   \n",
       "75%             638165.50000               48270.000000      7.987550   \n",
       "max             697017.00000               57270.000000      8.323367   \n",
       "\n",
       "       gpt-4-cost  total-cost  \n",
       "count   15.000000   15.000000  \n",
       "mean    11.084610   18.119829  \n",
       "std      1.778203    2.662672  \n",
       "min      7.478070   12.986423  \n",
       "25%     10.034445   16.107845  \n",
       "50%     11.076330   18.469598  \n",
       "75%     12.120585   20.091989  \n",
       "max     14.043030   21.794082  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usage_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 3))\n",
    "# stacked bar chart (gpt-3.5 + gpt-4)\n",
    "\n",
    "\n",
    "usage_df.plot.bar(x='app_name', y=['gpt-3.5-cost', 'gpt-4-cost'], stacked=True, ax=ax, color=['#f9c74f', '#f3722c'], legend='reverse')\n",
    "ax.set_ylabel('Cost (USD)')\n",
    "ax.set_xlabel('App Name')\n",
    "\n",
    "# add xticks rotation\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=85, horizontalalignment='right', fontsize=9)\n",
    "# legend position outside right \n",
    "ax.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0.)\n",
    "\n",
    "plt.title('API Usage Cost')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./figures/RQ4_cost.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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32t3c3LC2tmb9+vUm+/fs2cPff/9NlSpVzI4nI/Xr18fa2polS5YQHR39wATCnHPr6elJcnIyW7ZsMcokJiaaTEvKyvP7uG7dusX27dtNtrdt22asZUl7snf+/HmjzIkTJ9IlHve/Vene9Q9punfvzs2bNxkxYkS6C3eAKVOmEBMTQ48ePYx95nz+M3qjE8D3339vsr1x40YsLCyoWbOm0fa94wLSvUnrQW2LPC49gRARyULDhw/n559/Tndn18vLiw0bNuDm5kbJkiVZvXo1MTExWdZvnjx56NmzJwMGDCAlJYVp06ZRoEABOnbsCECnTp348ssv6dSpE126dKFgwYJ8/fXXLF++3HjFo7nKlCnDe++9x/Tp07l58ybVq1fnjz/+IDw8HA8PD5P58k+qT58+bNu2jQ4dOtCtWzesrKxYsmQJp0+fZs6cORnWqVevHu+88w5hYWE0bNiQEiVKMGDAAEaMGIGlpSX169fn+vXrREREcOHCBf7973+bHU/NmjUZOXIkEydOpG7duly/fp3w8HCcnZ154403zGqjQIECdOvWjRkzZmBlZUX9+vU5c+YM06ZNM87tk0hbozB79mxcXV0f+ETDnHPr6elJnTp1GDZsGP/88w/Fixdn0aJFxMbGGnf3LS0ts+z8PonAwED69+9PoUKFmDt3LgkJCcbbhzw8PLCxsWHChAl8/PHH3Lhxg+nTpxvridLkz5+fQ4cOERUVRYkSJTh48GC616+6uLgwYcIEAgMDadOmDW3btuW1117j4sWLrF69mp07dzJo0CCTdSLmfP7TnuSsX78eNzc34/cWHR3NoEGDaNq0KYcPHyYsLIyWLVsax+vXr8///vc//ve//+Hm5sb333+f7lvf09revHkzdevWNWvxtcjDKIEQEclCBQoUICgoiD59+pjsDwwMJDk5mYkTJ5I7d258fHwYOHAgw4YNy5J+K1SoQMOGDQkKCiIuLg5PT0+GDBliTHMoWrQoy5YtIyQkhKCgIG7fvo2zszPjxo0zXh+ZGePGjaNkyZKsWrWKzz77jCJFitChQwd69eqVpXc7y5YtyxdffMGUKVMIDAzEwsICV1dXFi1a9NA3Ew0ZMoQdO3YwfPhwFixYQIsWLciXLx9z5swhMjISW1tbqlSpQnBwcKamDLVu3ZqkpCSWLVvGF198gY2NDZ6engwePPiRC2vv1bdvX5ycnFiyZAmRkZEUKFCAd955h/79+z9yDYo5fHx8+PbbbzN8+1Iac89teHg4wcHBTJ8+ndu3b+Pj40PLli357rvvjDJZdX6fRFBQEJ9++imxsbFUqVKFpUuXUrJkSeDuBXRYWBghISH07t2b4sWL06dPn3Tf8dGlSxc+/fRTPvzwQ7p3707ZsmUznJbWuHFjXFxcWLBgAdOnTzemC1arVo2lS5eme5OSOZ////znP3z55ZcEBATw/vvvExQUBEDHjh25cOECffr0oWDBgvTo0YPu3bsb9bp3705sbCxz584lKSkJLy8vxo0bZyRPcDeBqlWrFiEhIezatYvZs2c/4dmWl51Fala8IkBEREQkG6R9CduRI0eyO5Qs5+LiQp8+fejbt292hyJiQpPiRERERETEbEogRERERETEbJrCJCIiIiIiZtMTCBERERERMZsSCBERERERMZsSCBERERERMZu+B0LkAfbt20dqamqm3u0uIiIi8jxKSkrCwsICd3f3R5bVEwiRB0hNTUXvGJCXSWpqKomJifq7F3kM+vzI8y4z1z16AiHyAGlPHipVqpTNkYg8GwkJCfzxxx+UKVMmS74NWeRlos+PPO8OHDhgdlk9gRAREREREbPpCYSIiIiISCZcvnyZuLi4p9a+vb09Tk5OT639J6UEQkRERETETJcvX2bgoEEkJSY+tT6srK0JCQ7OsUmEEggRERERETPFxcWRlJhIgdqu5HbIZ1ad5GvxXN15gAK1K5Hbwe4RZW9wdedvxMXFKYEQEREREXlR5HbIh1Uhh0zWsct0nZxIi6hFRERERMRsSiBERERERMRsSiBERERERMRsSiBERERE5KV3/fr17A7hgXJabEogREREROSlduHCBXr27MmFCxeyO5R0cmJsL2wC8ffff7Nhw4aHlklJSWH69OnUr18fV1dXmjdvztatWx9a58KFC7i4uKT7Wb16dRZGn72OHTv2yPNw5coVVqxYYXabu3fvxsXFhTNnzjxhdCIiIiJZKyEhgdTUVBISErI7lHRyYmwv7Gtc/f39KV68OI0bN35gmWnTprFixQrGjx9P6dKlWb9+Pb169WL58uVUrFgxwzqHDx8mT548bNmyBQsLC2O/vb19lo8hu3Tv3p333nsPLy+vB5aZNGkSZ86coUWLFs8uMBERERHJdi/sEwhzJCUlMXToULy8vChRogQ9e/YkX758/Pzzzw+sc/ToUZydnSlSpAiFCxc2fmxsbJ5h5NkvNTU1u0MQERERkWyQYxOI2NhYBgwYQLVq1fDw8CA4OJgOHToQFhZGWFgYbdq0YcaMGXh4eFCtWjUCAwOJj48HoH379kRFRbFmzRq8vb0f2Ie/vz9NmjQB4NatWyxevJibN2/i4eHxwDpHjhyhdOnSmRrL6tWrqVu3LsuXL6dOnTq4u7vTu3dvk7ls3t7eTJw4ER8fHzw8PIiKiiIlJYUFCxbQsGFDKlWqRMOGDVm6dKlRZ/fu3VSoUIHNmzfTsGFDXF1d6dChA+fOnWPs2LFUq1YNT09PZs6cadQJCAjgk08+YfTo0VSpUgVPT08mTJhA4v//OnZvb2/Onj1LeHg47du3z3A8AQEBrFmzhqioKFxcXAAeGev99uzZg7u7O1OnTjX2rVq1ikaNGuHq6kqjRo1YuHAhd+7cAeDMmTO4uLiwceNGWrRoQcWKFfH29iYyMtKo/88//9CvXz88PDxwdXWldevWREVFZeZXJSIiIiKPkCMTiDt37tC9e3diYmKYM2cO8+bNIzo62uRi8MCBA+zYsYN58+YxY8YMfvnlF/r37w9AWFgY7u7uNGrUiJUrVz6yv3Xr1lG5cmXGjh1Ljx49qFSp0gPLHj16lNjYWNq1a0etWrVo06YN27Zte2QfsbGxLFy4kNDQUBYuXMi5c+fo2rUrycnJRpklS5YwbNgw5syZQ+XKlZkwYQIRERH06dOHr776inbt2jFu3DgWLFhg1ElJSWHmzJkEBwezcOFCDh8+TNOmTbGysmLFihW0bt2a0NBQjhw5YtTZtGkTFy9eZNmyZYwdO5a1a9cybtw4AFauXMkrr7xCly5dCAsLy3AsQ4cOpVGjRri7u7Njxw4As2JNEx0dTbdu3ejcuTMDBgwAIDIykkmTJtGnTx82bNhA//79+eyzzwgODjapO378eHr06ME333yDl5cXQUFBnD59GoCgoCBu377NkiVL+Oqrr3j99dfp1atXjpozKCIiIvK8y5FrIKKiovjtt9/45ptvKFWqFAChoaEmTxMsLCwIDQ2laNGiAIwYMYKPPvqIkydPUqpUKaysrLCxscHR0fGR/VWvXp21a9eyc+dOpkyZgqOjI23btk1XLjk5mZMnT1KmTBkCAgKws7Njw4YNdOvWjfnz5+Pp6fnAPpKSkpg4caKxtmLy5Mn4+Piwa9cu3nzzTQDq1atHrVq1AIiPj2fp0qUEBATg6+sLgLOzM2fOnGH27Nl07NjRaPvjjz82kp6aNWuyf/9+/Pz8sLCwoHv37kRERHDs2DHjaUH+/PmZPHkyefPmpVy5cly8eJFx48YxePBgHB0dsbS0xNbWlgIFCmQ4Fnt7e2xsbLCysqJw4cKZivXgwYMMGzaMDz/8kN69exv7IyIi6Nmzp7FmpUSJEsTHxzNq1Cg+/vhjo1ynTp146623ABgwYACff/45+/fvp0SJEvz111+UK1eOEiVKYGNjw9ChQ/H19cXS0vKBvxcRERGRNGfPns2SMlkhrZ9n1V9m5MgE4tChQzg4OBjJA4CTkxOvv/66se3s7GwkDwBVqlQB7j4huLce3H3CMHLkSGO7atWqzJkzx9guVqwYxYoV44033iAmJoa5c+dmmEDkzp2b3bt3Y2lpaax5qFixIseOHWPu3Ll4enri7u5uUiftTVD58uUzWZhdunRpHBwcOHr0qJFAlCxZ0jh+8uRJkpKSqFq1qkl7NWrUYOHChfzzzz/Gvnvr2dra8tprrxkLvNPiTJuiBODq6krevHmNbXd3d5KSkjh16lS6py+zZs3if//7n7Ht6+vL6NGjTcpkJtbBgweTlJRE8eLFjX2xsbGcP3+eKVOmMG3aNGP/nTt3uH37NmfOnCFPnjzGeUuTtnA9KSkJgD59+jB48GA2btxI1apVqVOnDk2aNDHqioiIiDxMREREdodgyEmx3C9HJhCWlpbG3PcHsbKyMtlOSUkx6t7P29sbNzc3Y9vGxobk5GS2bt1KhQoVePXVV41jj3ola758+dLtK1u2rDGVZ+3atSbHihQpkmG8aTHfG++9C7EftEg57bzkzv1/v7p7/w2QK9fDZ6bdH0tamxmdu9atW9OoUSNj287OLl2ZzMTau3dvrl27xvjx46lduzaFCxc2ygUGBhpPYO5VrFgxLl68CIC1tfUD+3/77bfZvn0727dv56effmL+/PmEh4ezfPlyypYtm2GMIiIiIml69eplcpMzI2fPnn0mF/dpsTyr/jIjRyYQb7zxBnFxcZw4ccK443zlyhViYmKMMqdOnSIuLs64C71v3z4AKlSokK49Ozu7dBe+qampDB8+nPfff5+BAwca+/fv30+ZMmUyjOvYsWO0atWKmTNnmiy0PnjwoFHn3qcB97p69SqnT5+mRIkSRlvx8fEZxgt377RbWVnx66+/Ur58eWP/nj17KFy4MA4ODhnWM8fvv/9ukrzs27ePvHnzmjzhSVOgQIEMpzLd+wrbzMTapEkTChUqxKZNmwgKCmLGjBkUKlQIR0dHTp8+bXL+vv76azZv3szEiRMfOabExERCQkJo2rQpPj4++Pj4cOvWLWrXrs3WrVuVQIiIiMgjFS9ePMProeyQk2K5X45cRO3h4YGbmxt+fn5ER0dz+PBhBg0axM2bN40L14SEBPz8/Dh69Cg//fQTo0ePxsfHx8ga8+XLx9mzZzl//nyGfVhYWNClSxcWLVrEV199xZ9//sns2bNZv349ffv2NcrFxsYSFxcH3L1QLlWqFKNHj2bPnj2cOHGC8ePHEx0dTc+ePR85rsGDB3Pw4EGio6Px8/PD3d2d6tWrZ1jWzs6OVq1aMX36dNavX09MTAyff/45X3zxBV26dDG5gM+ss2fPMmrUKE6cOMGmTZuYPn06H3zwgTGtKV++fPz5559cvnz5gW3Y2tpy8eJFTp8+nelY8+bNy6hRo9iyZQvr16/HwsKCjz76iMWLF7NkyRL++usvNm/eTFBQEDY2Nhk+dbiftbU1Bw4cYPjw4URHR3PmzBlWr15NQkJCumllIiIiIvL4cuQTCLj7JqXRo0fTqVMn8uTJQ9u2bTl58iRWVlYkJiZSrFgxypcvT7t27bC0tMTX15dBgwYZ9Vu3bo2/vz/vvvsuu3btynB6zocffoiVlRVhYWGcO3eOUqVKMX36dGORLsD7779PjRo1mDBhArly5WLWrFmEhITQv39/rl+/ToUKFZg/fz7lypV75Jh8fX3p1q0biYmJeHt7M3To0IcmAoGBgRQsWJDg4GAuX76Ms7MzI0aMoGXLlpk8m6YqV65Mrly5eP/997G3t6dDhw4mCVD79u2ZOHEix44dY926dRm20axZMzZv3kyTJk3YtGlTpmOtU6cOTZs2ZcyYMXh6etKlSxfy5MnD4sWLmTBhAk5OTrRs2ZJ+/fqZPa6pU6cyfvx4evbsSVxcHKVKlSI4OJhq1apl7gSJiIiIyANZpObAbwSLjY1l//791KlTx5ivn5iYiIeHByNHjuT06dOsWbOG77//PpsjNc/q1asJDAw0eZVqdgkICODs2bMsXrw4u0PJ8Q4cOADw0Nf6irxIEhIS+OOPPyhfvjy2trbZHY7Ic0Wfn+fbqVOnGDp0KOPGjXvktKG0sk4+nlgVMm9KedI/17j89S6z6qSVTYslM7E9icxc9+TIKUy5c+dmwIABhISEEBMTw/Hjxxk5ciTW1tbUrVs3u8MTERERkReIra0tFhYWOTL5y4mx5cgpTPnz52fWrFmEhoYSGRlJrly5qFKlCosWLTLrex1ERERERMxVtGhRZs6cSf78+bM7lHRyYmw5MoGAu1+ItmzZsgyP9e3b12Shc07XvHlzmjdvnt1hAHe/MVpERERETOWkC/T75bTYcuQUJhERERERyZmUQIiIiIiIiNmUQIiIiIiIiNly7BoIEREREZGcKvnajUyUjTf536xqN7sogRARERERMZO9vT1W1tZc3flbpute3XnArHJW1tbY29tnuv1nRQmEiIiIiIiZnJycCAkOJi4u7qn1YW9vj5OT01Nr/0kpgRARERERyQQnJ6ccfYH/tGkRtYiIiIiImE0JhIiIiIiImE0JhIiIiIiImE1rIERERHKQy5cvZ8nizJy+CFNEnl9KIERERHKIy5cvM2jgQBKTkp64LWsrK4JDQpREiEiWUwIhIiKSQ8TFxZGYlMQHLo4UtX34f6IvJCSx5MgVPnApSFFbq/uOJbPkSCxxcXFKIEQkyymBEBERyWGK2uamhJ21mWWtzC4rIpIVtIhaRERERETMpgRCRERERETMpgRCRERERETMpgRCRETkIa5fv57dITwTL8s4ReTJKYEQERF5gAsXLtCzZ08uXLiQ3aE8VS/LOEUkayiBuMfff//Nhg0bzC6fmJiIr68vAQEBTzGqZ+/KlSusWLHioWWSkpJYsGBBptp1cXFh9erVTxCZiMizlZCQQGpqKgkJCdkdylP1soxTRLKGEoh7+Pv7s337drPLT5o0iaNHjz7FiLLHpEmTWLdu3UPLrF+/nvHjxz+jiEREREQkp1AC8Zi2b9/ON998Q9myZbM7lCyXmpqaJWVERERE5MXzQiUQsbGxDBgwgGrVquHh4UFwcDAdOnQgLCyMsLAw2rRpw4wZM/Dw8KBatWoEBgYSHx8PQPv27YmKimLNmjV4e3s/sp/AwEDGjBlDwYIFHxnXmTNncHFxYe3atTRp0gRXV1datmzJr7/+apQJCAigX79+dOnShSpVqvDZZ58BsHXrVlq2bIm7uzt16tRh/Pjx3Lp1y6jn4uJCZGQkbdu2pVKlSjRq1Ii9e/cSGRmJl5cXVapUoX///kad1atXU7duXZYvX06dOnVwd3end+/exrzXgIAA1qxZQ1RUFC4uLhmOZ/Xq1QQGBhr9796926xY73Xp0iXeeecdOnfubJTZu3cv7dq1w9XVFS8vL0aNGmX8fgC8vb2ZO3cuffv2xd3dHQ8PD8aOHUtycjIAKSkpTJ48mXr16lGxYkXeeecdli5d+sjfj4iIiIiY74VJIO7cuUP37t2JiYlhzpw5zJs3j+joaKKioowyBw4cYMeOHcybN48ZM2bwyy+/0L9/fwDCwsJwd3enUaNGrFy58qF9DR06lPr16z8y0bjfhAkT6NGjB2vWrKFUqVJ06dKF06dPG8c3btxIrVq1WLVqFU2aNGHz5s307NkTLy8vVq9ezahRo/j666/55JNPTNqdOnUqXbt25csvv8Te3p4ePXqwceNGZs+ezfjx49myZYvJmobY2FgWLlxIaGgoCxcu5Ny5c3Tt2pXk5GSGDh1Ko0aNcHd3Z8eOHRmOw8fHhyFDhgCwY8cO3N3dzY41rf9OnTpRvHhxZs2ahY2NDYcPH6Zz5868+eabrFu3juDgYH7//Xe6dOli8rRj2rRpVK9enXXr1uHn58eSJUtYv349AF988QXffvstU6dOZePGjXzwwQcEBQWxZ8+eTP2eREREROTBcmd3AFklKiqK3377jW+++YZSpUoBEBoaanKRb2FhQWhoKEWLFgVgxIgRfPTRR5w8eZJSpUphZWWFjY0Njo6OD+xn2bJlnDhxgpCQkEzH2K1bN5o0aQLAmDFj+Pnnn1m+fDkDBw4EwMHBga5duxrl+/Xrx9tvv02vXr0AeP3110lNTaV3794cP36cMmXKAPDf//7XGGfTpk0ZPXo0I0aMwNnZmXLlyjFnzhyOHTtmtJuUlMTEiROpWLEiAJMnT8bHx4ddu3bx5ptvYmNjg5WVFYULF85wHDY2Ntjb2wMYZWbPnm1WrFevXqVTp068+uqrzJgxA2trawDmzp1L7dq16dGjBwDOzs6EhITQoEEDoqKi8PDwAKBOnTp06NABgBIlSrB48WL27t1Ls2bN+Ouvv7C1teW1116jSJEifPDBB5QqVYrXX389078rEZF7nT179rnsx9z2ntX4ROTF8MIkEIcOHcLBwcFIHgCcnJxMLh6dnZ2N5AGgSpUqABw9etSkHsC6desYOXKksV21alWGDBnC5MmTmTt3Lra2thnG0bVrV5OpSaNGjTL6SbsIBrCysqJixYomi7BLlixp0tbRo0dp3Lixyb4aNWoYx9Iuyu+tlzdvXgD+9a9/GftsbGxITEw0tvPly2ckDwClS5fGwcGBo0eP8uabb5r0t2fPHj766CNj+9VXX83wTVXmxjp16lSSkpKoWLGikTzA3d9fTEwM7u7u6do+ceKEce5Kly5tcsze3p6kpCQA2rVrx5YtW6hXrx7ly5endu3aNG7cmEKFCqVrU0QkMyIiIrI7hMfyvMYtIjnbC5NAWFpacufOnYeWsbKyMtlOSUkx6t7P29sbNzc3Y9vGxoYVK1Zw48YNOnfubOy/desWe/fuZePGjezbt49x48aZzPsvVKgQV69eBSB3btPTnZKSQq5c/zeLzMbGxuR4RguV08Z4b1v3twuYtHu/+89DWiwZnYeKFSuydu3ah/aVmVhr1arFf//7X/r27YuPjw916tQxyvr6+hpPIO517xOhe5OO+/t2dnZm06ZNREVFsXPnTrZu3cpnn33G+PHjee+99zKMW0TEHL169aJ48eJPvZ+zZ89m6UW/uXFndb8i8mJ7YRKIN954g7i4OE6cOGHcpb5y5QoxMTFGmVOnThEXF2dMv9m3bx8AFSpUSNeenZ0ddnZ2Jvs++OADfH19TfYNGjSIV155hUGDBgGYPOFIk5ZAHDhwwFiYnJiYyO+//07Lli0fOCYXFxf27t1Lp06djH1p8/nvvxOfGVevXuX06dOUKFECgGPHjhEfH2+cBwsLC6OsjY1Nuicj95fJTKwNGzbkP//5Dz4+PgwfPpyvvvoKOzs7ypYty/Hjx036OnHiBJMnT+aTTz4xfmcPs2jRIgoVKkTjxo2pXbs2fn5+dO7cma+//loJhIg8keLFiz+X0yGf17hFJGd7YRZRe3h44Obmhp+fH9HR0Rw+fJhBgwZx8+ZN42I3ISEBPz8/jh49yk8//cTo0aPx8fEx7s7ky5ePs2fPcv78+Qz7KFCgACVLljT5sbGxIV++fBleZN8vNDSUrVu3cvz4cYYMGcLNmzcfmkB07dqVTZs2ERERwalTp/jhhx8YM2YM9evXf6IEAmDw4MEcPHiQ6Oho/Pz8cHd3p3r16gDY2tpy8eJFkwXe90ubwnXw4EFu3bqV6ViHDh3KjRs3mDRpEgBdunTh0KFDjBo1ihMnTrBv3z4GDhzIn3/+ibOzs1ljio2NZfTo0Xz33XecPXuW7du388cff2Q4LUpEREREHs8L8wQC7r5JafTo0XTq1Ik8efLQtm1bTp48iZWVFYmJiRQrVozy5cvTrl07LC0t8fX1NZ4cALRu3Rp/f3/effdddu3aleGUnifRpk0bJk6cyN9//42bmxuLFy+mSJEiDyzfsGFDpkyZwsyZM4mIiMDR0ZEmTZrQr1+/J47F19eXbt26kZiYiLe3N0OHDjUSrWbNmrF582aaNGnCpk2bMnyqUrNmTdzc3GjdujWTJ0+mUaNGmYrVyckJPz8/461Pnp6ezJkzh2nTpvHee+9ha2uLp6cn/v7+GU5bykifPn1ISkpi7NixXLp0icKFC9OmTRu6d+/++CdKRERERExYpL4g3wgWGxvL/v37qVOnjjHHPzExEQ8PD0aOHMnp06dZs2YN33///TOP7cyZM7z11lssWrTIZCF1dkj7DocjR45kaxzPgwMHDgBQqVKlbI5E5NlISEjgjz/+oHz58g98UcTL5tSpUwwdOpRx48Y9k6lAaf0NdC9CCbuH3zw5HZ9IyL6LGZZNO2Zu3M96nC8ifX7keZeZ654XZgpT7ty5GTBgACEhIcTExHD8+HFGjhyJtbU1devWze7wREREREReCC9MApE/f35mzZpFdHQ0zZo1o1WrVly+fJlFixY99HsdREREHsTW1hYLC4sX/o7yyzJOEckaL9QaiJo1a7Js2bIMj/Xt25e+ffs+44jueu2113LMlKHmzZvTvHnz7A5DROS5ULRoUWbOnEn+/PmzO5Sn6mUZp4hkjRfmCYSIiMjT8LJcVL8s4xSRJ6cEQkREREREzKYEQkREREREzPZCrYEQERF5EVxISDajTJLJ/2a2vojI41ICISIikkPY29tjbWXFkiOxZtdZcuRKhvutraywt7fPqtBERAxKIERERHIIJycngkNCiIuLe+K27O3tcXJyyoKoRERMKYEQERHJQZycnHThLyI5mhZRi4iIiIiI2ZRAiIiIiIiI2ZRAiIiIiIiI2bQGQkREXnqXL19+5MJlLUoWEblLCYSIiLzULl++zMBBg0hKTHxoOStra0KCg5VEiMhLTwmEiIi81OLi4khKTKScWwMAju7fQjm3BtjaFTTKJMRf4ej+LcTFxSmBEJGXnhIIERERMEkYbO0KYudQOBujERHJubSIWkREREREzKYEQkREREREzKYEQkREREREzKYEQkREXkjXr19/rtsXEcmplECIiMgL58KFC/Ts2ZMLFy48l+2LiORkzzSB+Pvvv9mwYcNDy6SkpDB9+nTq16+Pq6srzZs3Z+vWrQ+tc+HCBVxcXNL9rF69Ogujf/rOnDmDi4sLu3fvNrvOsWPHTM7Psxh3amoqa9as4Z9//jG7jre3N2FhYU8xKhGR/5OQkEBqaioJCQnPZfsiIjnZM32Nq7+/P8WLF6dx48YPLDNt2jRWrFjB+PHjKV26NOvXr6dXr14sX76cihUrZljn8OHD5MmThy1btmBhYWHst7e3z/Ix5DTdu3fnvffew8vLC4AdO3Y89XH/8ssvBAQE8N133z3VfkREREQk58lxU5iSkpIYOnQoXl5elChRgp49e5IvXz5+/vnnB9Y5evQozs7OFClShMKFCxs/NjY2zzDynOFZjDs1NfWpti8iIiIiOVemEojY2FgGDBhAtWrV8PDwIDg4mA4dOhAWFkZYWBht2rRhxowZeHh4UK1aNQIDA4mPjwegffv2REVFsWbNGry9vR/Yh7+/P02aNAHg1q1bLF68mJs3b+Lh4fHAOkeOHKF06dKZGQoAv/32G506dcLd3Z1atWoxcuRIbt68CdydSrVgwQIaNmxIpUqVaNiwIUuXLjXq7t69mwoVKrB582YaNmyIq6srHTp04Ny5c4wdO5Zq1arh6enJzJkzjToBAQF88sknjB49mipVquDp6cmECRNITEx8YIyrVq2iUaNGuLq60qhRIxYuXMidO3eAu9OCzp49S3h4OO3btwfST2Fau3Yt7777Lq6urnh7exMREUFKSgrwf1OmNm7cSIsWLahYsSLe3t5ERkY+MJ7du3fToUMHAN566y2jr3379tGhQweqVq2Kh4cHgYGBXLlyJcM2bty4QZs2bXj33XeJjY0F4MSJE3z00Ue4u7tTp04dBg4cyKVLl4w67du3Jzg4mCFDhlCtWjWqVKnCwIEDjb8vgLlz59KgQQNjHDNmzFCyIyIiIpLFzE4g7ty5Q/fu3YmJiWHOnDnMmzeP6OhooqKijDIHDhxgx44dzJs3jxkzZvDLL7/Qv39/AMLCwnB3d6dRo0asXLnykf2tW7eOypUrM3bsWHr06EGlSpUeWPbo0aPExsbSrl07atWqRZs2bdi2bdtD2z99+jQdO3akSJEiREZGEhYWxs6dOxk1ahQAEyZMICIigj59+vDVV1/Rrl07xo0bx4IFC4w2UlJSmDlzJsHBwSxcuJDDhw/TtGlTrKysWLFiBa1btyY0NJQjR44YdTZt2sTFixdZtmwZY8eOZe3atYwbNy7DGCMjI5k0aRJ9+vRhw4YN9O/fn88++4zg4GAAVq5cySuvvEKXLl0yXF+wYMEChg8fTqtWrVi3bh0ff/wxc+fOZcKECSblxo8fT48ePfjmm2/w8vIiKCiI06dPZxiTu7u70deKFSvw8fHht99+o3379pQtW5bly5czbdo09u/fz4cffmgkK2lu3rxJjx49uHXrFosWLcLR0ZELFy7Qtm1bSpYsycqVK5k1axbx8fG0atXKZH7xggULcHJyYuXKlUyePJnvvvvO+H18//33/O9//2PUqFFs2rSJQYMGMXPmTNatW5fhOERERETk8Zi9BiIqKorffvuNb775hlKlSgEQGhpq8jTBwsKC0NBQihYtCsCIESP46KOPOHnyJKVKlcLKygobGxscHR0f2V/16tVZu3YtO3fuZMqUKTg6OtK2bdt05ZKTkzl58iRlypQhICAAOzs7NmzYQLdu3Zg/fz6enp4Ztr98+XIKFCjAp59+Su7cd0/D2LFj2bdvH/Hx8SxdupSAgAB8fX0BcHZ25syZM8yePZuOHTsa7Xz88cdGclOzZk3279+Pn58fFhYWdO/enYiICI4dO4aLiwsA+fPnZ/LkyeTNm5dy5cpx8eJFxo0bx+DBg9PFGBERQc+ePY01IyVKlCA+Pp5Ro0bx8ccf4+joiKWlJba2thQoUMCkbmpqKp999hkffPAB7dq1M8Zw9epVJk+eTL9+/YyynTp14q233gJgwIABfP755+zfv58SJUqki8na2hoHBwcAHB0dsbGxYd68ebi4uDB8+HAASpcuzZQpU2jatCk7duygXr16ANy+fZuePXty48YNFixYYLSzdOlSXnnlFYYNG2b0ExoaSs2aNfn2229p3rw5AGXKlOGTTz4xxlK7dm327dsHwF9//YW1tTXFixfn1Vdf5dVXX6VIkSK8+uqrGf7+ReTlcPbs2Swpc3/ZzNQREXnRmJ1AHDp0CAcHByN5AHBycuL11183tp2dnY3kAaBKlSrA3ScE99aDu08YRo4caWxXrVqVOXPmGNvFihWjWLFivPHGG8TExDB37twME4jcuXOze/duLC0tjbn/FStW5NixY8ydOxdPT0/c3d1N6mzYsIGjR4/y73//20ge4G4CULNmTX777TeSkpKoWrWqSb0aNWqwcOFCk7cPlSxZ0vi3ra0tr732mrGQOy2ee6coubq6kjdvXmPb3d2dpKQkTp06RcGCBY39sbGxnD9/nilTpjBt2jRj/507d7h9+zZnzpx56LSt2NhYLl++nOEYkpKSOHnyJIUKFQIwaSdtAXZSUhJ///13ugXvaRfs9zp69Ci1a9c22ffGG29gb2/PkSNHjARi4cKFJCUlUbNmTSN5gLt/W8eOHUv3e7p9+zYnTpwwtu//G7K3tzfew/7uu++yatUqGjZsSJkyZahVqxYNGzZUAiHykouIiMjR7YmIPI/MTiAsLS2NufcPYmVlZbKdNn3F0tIyXVlvb2/c3NyMbRsbG5KTk9m6dSsVKlQwufB71KtJ8+XLl25f2bJl2bFjB3B3HcC9ihQpYpI43O9B8+bTxn9v3fvbyZXr4bPC7j9HaW3ef47S9gcGBlKrVq107RQrVuyh/WRmDNbW1hnWL1KkSLpzl5m+UlNTTcZbrlw5/P396dy5M5GRkbRq1cqIqWbNmiYJZZp73yiVUZxpHB0d+fLLL9m3bx87d+5kx44dLFq0iL59+9KnT59HjkFEXky9evWiePHiDy1z9uxZsxODtPYyU0dE5EVj9hqIN954g7i4OJM7wleuXCEmJsbYPnXqFHFxccZ22t3qChUqpGvPzs6OkiVLGj9FixbF0tKS4cOHmyxWBti/fz9lypTJMK5jx45RpUqVdN+dcPDgQaPOvf2ULFmS3LlzU6ZMGQ4dOmQyR3/z5s14e3tTunRprKys+PXXX03a3LNnD4ULFza5e55Zv//+u0mf+/btI2/evCZPcgAKFSqEo6Mjp0+fNon9999/JzQ09JH9ODk54eTklOEYrKys+Ne//vXINnLnzp3u3AEmr8qFuwne/f0cPnyY+Ph4k6cbXl5e1KhRg86dOzNp0iTOnTsH3E32Tpw4QbFixYx+HBwc+PTTTzl69Ogj44S7T7SWLl1K1apV6devH8uXL6dFixZ8/fXXZtUXkRdT8eLFef311x/686gEI6P2MlNHRORFY3YC4eHhgZubG35+fkRHR3P48GEGDRrEzZs3jQvKhIQE/Pz8OHr0KD/99BOjR4/Gx8fH+D/afPnycfbsWc6fP59hHxYWFnTp0oVFixbx1Vdf8eeffzJ79mzWr19P3759jXKxsbFGolK6dGlKlSrF6NGj2bNnDydOnGD8+PFER0fTs2fPB46nbdu2XLlyhZEjR3LixAl++eUXJk2aRM2aNbGzs6NVq1ZMnz6d9evXExMTw+eff84XX3xBly5d0l1AZ8bZs2cZNWoUJ06cYNOmTUyfPp0PPvjAZFpT2rn46KOPWLx4MUuWLOGvv/5i8+bNBAUFYWNjY9yNz5cvH3/++SeXL19O19eHH37IkiVL+OKLL4iJieGrr74iPDycVq1aPdF3Rdja2gJ3k4QbN27QuXNnjhw5wpgxYzhx4gS7d+9m0KBBVKhQIcM1KH369MHR0dFY89C2bVvi4uIYNGgQhw8f5vDhwwwYMIADBw5Qrlw5s2K6ffs2EydOZO3atZw5c4Y9e/bwyy+/pJsWJSIiIiJPJlNfJBcWFsbo0aPp1KkTefLkoW3btpw8eRIrKysSExMpVqwY5cuXp127dlhaWuLr68ugQYOM+q1bt8bf3593332XXbt2ZTi16cMPP8TKyoqwsDDOnTtHqVKlmD59urHIF+D999+nRo0aTJgwgVy5cjFr1ixCQkLo378/169fp0KFCsyfP/+hF59FixZl3rx5TJ48mWbNmuHg4ICPj4+xSDcwMJCCBQsSHBzM5cuXcXZ2ZsSIEbRs2TIzpyydypUrkytXLt5//33s7e3p0KHDAxOdLl26kCdPHhYvXsyECRNwcnKiZcuWJgug27dvz8SJEzl27Fi6Nw516dIFa2trFi5cyKeffsorr7zCRx99xIcffvhEYyhXrhz16tWjf//+fPLJJ3Tp0oU5c+YQGhpKs2bNsLOzo0GDBgwcODDdlC24O10t7e9oxYoVtGjRgiVLlhASEkKbNm2wtLSkSpUqxluazNGiRQuuXr1KREQE586dw8HBgYYNG5r8/YmIiIjIk7NINfNF+bGxsezfv586deoYF4WJiYl4eHgwcuRITp8+zZo1a/j++++fasDPs4CAAM6ePcvixYuzOxQxw4EDBwAe+gphkRdJQkICf/zxB+XLlzeeND6vTp06xdChQxk3bly6KaIPKlu5dgsAoneuoHLtFtg5FDbKxF+7RPTOFUZ7mWlfXg4v0udHXk6Zue4xewpT7ty5GTBgACEhIcTExHD8+HFGjhyJtbU1devWffxoRURERETkuWF2ApE/f35mzZpFdHQ0zZo1o1WrVly+fDlT00xERESeBVtbWywsLJ7aneCn3b6ISE6WqTUQNWvWZNmyZRke69u3r8lCZ0nv/m+AFhGRp6No0aLMnDmT/PnzP5fti4jkZGY/gRAREXmePO2LeyUPIvKyUgIhIiIiIiJmUwIhIiIiIiJmUwIhIiIiIiJmy9QiahERkRdVQvyVDP+d0baIyMtMCYSIiLzU7O3tsbK25uj+Lca+e/+dxsraGnt7+2cZmohIjqQEQkREXmpOTk6EBAcTFxf30HL29vY4OTk9o6hERHIuJRAiIvLSc3JyUnIgImImLaIWERERERGzKYEQERERERGzKYEQERERERGzaQ2EyHPq8uXLj1z0mVlaJCoiIiKPogRC5Dl0+fJlBg0cRGJSYpa2a21lTXBIsJIIEREReSAlECLPobi4OBKTEvmvizeFbQuYVedSwhVWHfmB/7rUp7BtwQyOX2XVke+Ji4tTAiEiIiIPpARC5DlW2LYAr9oVzmSdgpmuIyIiIpJGi6hFRERERMRsSiBERERERMRsSiBEcoDr169ndwhP1Ys+PhERkZeJEgiRbHbhwgV69uzJhQsXsjuUp+JFH5+IiMjLRgmESDZLSEggNTWVhISE7A7lqXjRxyciIvKyyZEJxN9//82GDRvMLp+YmIivry8BAQFPMaqnx9vbm7CwMLPLX7lyhRUrVhjb7du3f+Zjvz+GR9m9ezcuLi6cOXPmKUYlIiIiIk9bjkwg/P392b59u9nlJ02axNGjR59iRDnLpEmTWLdunbEdFhbG0KFDszUGEREREXk5PPffA7F9+3a++eYbypYtm92hPDOpqakm2wUKFMj2GERERETk5fBUnkDExsYyYMAAqlWrhoeHB8HBwXTo0IGwsDDCwsJo06YNM2bMwMPDg2rVqhEYGEh8fDxwdzpOVFQUa9aswdvb+5H9BAYGMmbMGAoWTP/Nuhm5fPkyfn5+eHh4ULVqVbp3705MTIxxfOvWrbRs2RJ3d3fq1KnD+PHjuXXrlnHcxcWFyMhI2rZtS6VKlWjUqBF79+4lMjISLy8vqlSpQv/+/Y06q1evpm7duixfvpw6derg7u5O7969H7qgdO/evbRr1w5XV1e8vLwYNWqUcX4CAgJYs2YNUVFRuLi4GOfs3ilM+/bto0OHDlStWhUPDw8CAwO5cuWKcdzb25u5c+fSt29f3N3d8fDwYOzYsSQnJ5t1DjOKISUlhQULFtCwYUMqVapEw4YNWbp06QPb2LNnD+7u7kydOtXYt2rVKho1aoSrqyuNGjVi4cKF3LlzB4AzZ87g4uLCxo0badGiBRUrVsTb25vIyEij/j///EO/fv3w8PDA1dWV1q1bExUVZdaYRERERMQ8Wf4E4s6dO3Tv3p2UlBTmzJmDlZUV48ePZ8+ePVSvXh2AAwcOADBv3jzi4+MZOnQo/fv3Z86cOYSFhdGjRw9eeeUVRowY8dC+hg4dSv369fH29mb+/PmPjC05OZkuXbqQO3duIiIiKFCgABMmTKBr1658++23fP/99/Tr14++ffsyceJETp48SVBQEKdPnyYiIsJoZ+rUqXz66ac4OzsTEBBAjx49qFixIrNnz+bUqVMMHDiQFStW0L59e+BuorNw4UJCQ0OxtrYmKCiIrl27smbNGnLnNv0VHD58mM6dO9OzZ0/GjRvH5cuXmTRpEl26dCEyMpKhQ4dy69Ytzp8/n+G6id9++4327dvTqlUrRo4cyaVLlxg9ejQffvghK1aswNLSEoBp06YxaNAg/Pz8iIqKYujQoVSsWJFmzZo98jxmFMOECRP48ssvGT58OJUqVWLbtm2MGzeO27dv06lTJ5P60dHRdOvWjc6dO9OvXz8AIiMjmTJlCiNGjMDV1ZVDhw4xZswYLly4gJ+fn1F3/PjxDB8+nHLlyjF//nyCgoKoVasWJUqUICgoiMTERJYsWYK1tTWzZs2iV69ebNu2DVtb20eOK7udPXv2qZR9mnFkR3siIiKSvbI8gYiKiuK3337jm2++oVSpUgCEhoaaPE2wsLAgNDSUokWLAjBixAg++ugjTp48SalSpbCyssLGxgZHR8cH9rNs2TJOnDhBSEiI2bHt2rWLI0eO8O233/L6668DMHbsWBYsWMC1a9eYPXs2b7/9Nr169QLg9ddfJzU1ld69e3P8+HHKlCkDwH//+19jPE2bNmX06NGMGDECZ2dnypUrx5w5czh27JjRb1JSEhMnTqRixYoATJ48GR8fH3bt2sWbb75pEuPcuXOpXbs2PXr0AMDZ2ZmQkBAaNGhAVFQUHh4e2NjYYGVlReHChdONcd68ebi4uDB8+HAASpcuzZQpU2jatCk7duygXr16ANSpU4cOHToAUKJECRYvXszevXvNSiDs7e1NYoiPj2fp0qUEBATg6+trxH3mzBlmz55Nx44djboHDx5k2LBhfPjhh/Tu3dvYHxERQc+ePWncuLERU3x8PKNGjeLjjz82ynXq1Im33noLgAEDBvD555+zf/9+SpQowV9//UW5cuUoUaIENjY2DB06FF9fXyNpyunuTVKzU06JQ0RERHKmLE8gDh06hIODg5E8ADg5ORkX7HD34jIteQCoUqUKAEePHjWpB7Bu3TpGjhxpbFetWpUhQ4YwefJk5s6d+8A7y127duXXX381tkeNGsWlS5dwcHAwiaVo0aL4+/sb/addwKapUaOGcSwtgShZsqRxPG/evAD861//MvbZ2NiQmJhobOfLl89IHuDuRb2DgwNHjx5Nl0AcOnSImJgY3N3d043pxIkTeHh4ZDjeNEePHqV27dom+9544w3s7e05cuSIkUCULl3apIy9vT1JSUkPbftBTp48SVJSElWrVjXZX6NGDRYuXMg///xj7Bs8eDBJSUkUL17c2BcbG8v58+eZMmUK06ZNM/bfuXOH27dvc+bMGfLkyZMubnt7ewAj7j59+jB48GA2btxI1apVqVOnDk2aNDHq5nS9evUyOS8Pc/bs2ad2oZ+ZOMzxNGMVERGRZy/LEwhLS0tj3vqDWFlZmWynpKQYde/n7e2Nm5ubsW1jY8OKFSu4ceMGnTt3NvbfunWLvXv3snHjRvbt28e4ceNM1i4UKlSIVatWPTSujBYGp43l3qlG9087AsiV68HLSe4fL9wdc0bjvXPnDr6+vsYTiHs97IlMmgctbk5NTTWJw9ra2uy6j9tnRueud+/eXLt2jfHjx1O7dm0KFy5slAsMDKRWrVrp2ilWrBgXL158ZNxvv/0227dvZ/v27fz000/Mnz+f8PBwli9f/lwssi9evLhJcvuyxyEiIiI5U5Yvon7jjTeIi4vjxIkTxr4rV66YLFQ+deoUcXFxxva+ffsAqFChQrr27OzsKFmypPFTtGhRPvjgAzZu3MjatWuNn7RFtWvXrgXuPlm4t56dnR1lypTh2rVrJrHExsbi4eFBdHQ0Li4u7N2716T/PXv2AOnv2GfG1atXOX36tLF97Ngx4uPjMxxv2bJlOX78uEnsycnJjB8/nnPnzgF3p4A9iIuLi8mTF7i7riI+Pv6JxnC/e2MoXbo0VlZW6frds2cPhQsXxsHBwdjXpEkT+vXrh52dHUFBQcDd5M7R0ZHTp0+bjPv3338nNDTUrHgSExMZP348p0+fxsfHh7Fjx7JlyxZy5crF1q1bn3S4IiIiIvL/ZXkC4eHhgZubG35+fkRHR3P48GEGDRrEzZs3jYvOhIQE/Pz8OHr0KD/99BOjR4/Gx8fHmDaRL18+zp49y/nz5zPso0CBAiYXmiVLlsTGxoZ8+fKZTC+6n6enJxUrVsTf35/ffvuNY8eO4e/vj6OjI//+97/p2rUrmzZtIiIiglOnTvHDDz8wZswY6tev/8QX34MHD+bgwYNER0fj5+eHu7u7saj8Xl26dOHQoUOMGjWKEydOsG/fPgYOHMiff/6Js7MzALa2tly8eNEkKUnTuXNnjhw5wpgxYzhx4gS7d+9m0KBBVKhQAU9Pzycaw73ujcHOzo5WrVoxffp01q9fT0xMDJ9//jlffPEFXbp0SZfw5M2bl1GjRrFlyxbWr1+PhYUFH330EYsXL2bJkiX89ddfbN68maCgIGxsbDJ86nA/a2trDhw4wPDhw4mOjubMmTOsXr2ahISEDKeDiYiIiMjjeSrfAxEWFsbo0aPp1KkTefLkoW3btpw8eRIrKysSExMpVqwY5cuXp127dlhaWuLr68ugQYOM+q1bt8bf3593332XXbt2Zdki2Fy5chEREcH48ePp3LkzFhYW1KxZ03hbVMOGDZkyZQozZ84kIiICR0dH4475k/L19aVbt24kJibi7e3N0KFDM3ySULlyZebMmcO0adN47733sLW1xdPTE39/f+NCulmzZmzevJkmTZqwadMmk/pubm7MmTOH0NBQmjVrhp2dHQ0aNGDgwIEZTqV6XPfHEBgYSMGCBQkODuby5cs4OzszYsQIWrZsmWH9OnXq0LRpU8aMGYOnpyddunQhT548LF68mAkTJuDk5ETLli0zde6nTp3K+PHj6dmzJ3FxcZQqVYrg4GCqVauWVcMWEREReelZpGbxN4LFxsayf/9+6tSpY1ywJiYm4uHhwciRIzl9+jRr1qzh+++/z8puc6zVq1cTGBjIkSNHsjsUyaS01w1XqlTpqfZz6tQphg4dyrhx48xee5BWp4d7c161S/82roz8HX+JWftWP7BO2vHMxJGZWLO6Xcl6CQkJ/PHHH5QvX/65ePWxSE6iz4887zJz3ZPlU5hy587NgAEDCAkJISYmhuPHjzNy5Eisra2pW7duVncn8tyztbXFwsLihf0Pzos+PhERkZdNlk9hyp8/P7NmzSI0NJTIyEhy5cpFlSpVWLRokVlvEZLsNXr0aNasWfPQMjNmzMjwbUnyeIoWLcrMmTPJnz9/dofyVLzo4xMREXnZPJU1EDVr1mTZsmUZHuvbty99+/Z9Gt3mSM2bN6d58+bZHYbZ+vTpY/LFbxkpUqTIM4rm5fGiX1y/6OMTERF5mTyVBEKeX46OjnpSJCIiIiIPlOVrIERERERE5MWlBEJERERERMymKUwiz7FLCVczUfaKyf8+SVsiIiLy8lICIfIcsre3x9rKmlVHMv99KquO/PDAY9ZW1tjb2z9JaCIiIvKCUwIh8hxycnIiOCSYuLi4LG3X3t4eJyenLG1TREREXixZ/k3UIi+KvXv3kpqairW1dXaHIvJMpKamkpSUhJWVFRYWFtkdjshzRZ8fed4lJiZiYWFBlSpVHllWTyBEHkD/AZCXjYWFhRJmkcekz4887ywsLMy+9tETCBERERERMZte4yoiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImbLnd0BiIhIztehQweuXLlCrlx37zvNmzePQoUKZXNUIs+P9evXM3fuXBISEujatSstWrTI7pBEHpsSCBEReajU1FTOnj3Lli1bsLCwyO5wRJ47p06dYurUqaxatQpLS0t8fX1p0KABBQsWzO7QRB6LpjCJiMhDnTx5kpSUFDp27Mh7773Hpk2bsjskkefKd999x3vvvUeBAgWwt7dn0aJF2NraZndYIo9NCYSIiDzU9evXqVmzJrNnzyYiIoIJEyZw+vTp7A5L5Llx+vRpbty4QYcOHWjatCl79+4lT5482R2WyGPTFCYREXkod3d33N3dAShWrBje3t78/PPPlChRIpsjE3k+JCcnExUVxYIFC0hKSqJ169a4urpSqlSp7A5N5LHoCYSIiDzUr7/+ys8//2yyL3du3X8SMZeTkxN16tTB3t4eR0dHqlatyuHDh7M7LJHHpgRCREQeKj4+npCQEBITE4mNjWXr1q3UqlUru8MSeW7UrVuXH3/8kZs3b3Ljxg2io6NxcXHJ7rBEHptuIYmIyEPVq1ePvXv30qxZM+7cucMnn3xC0aJFszsskedG1apVef/993n//fdJTk7mgw8+oHTp0tkdlshjs0hNTU3N7iBEROTZ+N///seOHTtYvHixse/OnTuEh4ezYsUK4uLiqF69OiNGjNAaB5EM6DMkoilMIiIvjc8//5zQ0NB0+yMiIvjiiy8YM2YMy5Yt486dO3Tt2pXExMRnH6RIDqbPkMhdSiBERF5wFy5coEePHgQHB+Ps7GxyLDExkXnz5tGvXz+8vLx44403mDp1KufPn9f3PYj8f/oMiZhSAiEi8oL7/fffsbKyYt26dbi5uZkcO3z4MDdu3MDT09PYlz9/fipUqMAvv/zyrEMVyZH0GRIxpUXUIiIvOG9vb7y9vTM8dv78eeDu9zvcq0iRIsYxkZedPkMipvQEQkTkJXbz5k0ArK2tTfbnyZOH27dvZ0dIIs8VfYbkZaQEQkTkJWZjYwOQbrHn7du3yZs3b3aEJPJc0WdIXkZKIEREXmJp0y4uXrxosv/ixYv6rgcRM+gzJC8jJRAiIi+xN954Azs7O3bv3m3su379OocOHaJ69erZGJnI80GfIXkZaRG1iMhLzNramg8++IDg4GAcHR0pXrw4kydP5pVXXuE///lPdocnkuPpMyQvIyUQIiIvuX79+pGcnMywYcO4desW1atXZ+7cuVhZWWV3aCLPBX2G5GVjkZqamprdQYiIiIiIyPNBayBERERERMRsSiBERERERMRsSiBERERERMRsSiBERERERMRsSiBERERERMRsSiBERERERMRsSiBERERERMRsSiBERERERMRsSiBERCRHaN++PRUqVODAgQMZHvf29iYgIOCZxBIQEIC3t/cz6SszkpOTCQgIwN3dnSpVqvDzzz+bHF+9ejUuLi6P/HmUM2fO4OLiwurVq5/WUETkOZY7uwMQERFJk5KSQmBgIKtXr8ba2jq7w8lxtm/fzpo1a+jVqxe1atWiQoUKJse9vLyIjIw0trdu3crMmTMJDw+ncOHCzzpcEXlBKYEQEZEcw97enmPHjjFjxgwGDBiQ3eHkOFevXgWgefPmlChRIt1xR0dHHB0dje2TJ08CUL58eV577bVnEqOIvPg0hUlERHKM8uXL06xZM+bMmcPBgwcfWtbFxYWwsDCTfWFhYSZTdAICAvjwww+JjIykQYMGuLq60rp1a06dOsUPP/yAr68vbm5utGjRgj/++CNdH5GRkXh5eeHq6krHjh05dOiQyfG///6bTz75hBo1auDm5pauTNpUoPnz5/POO+/g5ubGqlWrMhxPSkoKn3/+Ob6+vri6uuLl5UVwcDC3b982xpI2hatBgwa0b9/+oefnYf7880/69etH7dq1qVy5Mu3bt+fXX399YPnU1FQCAwNxdXVlx44dxv4tW7bQvHlzKlWqRO3atRk7diwJCQnG8bCwMN5++222bt2Kr68vFStWpGHDhqxdu9ak/YULF/LOO+9QqVIl3nzzTYKCgoiPj3/s8YnI06UnECIikqMMGTKEnTt3EhgYyKpVq554KtO+ffu4ePEiAQEB3L59m6CgILp164aFhQX9+vUjb968jBw5kkGDBrFhwwaj3vnz5wkPD2fgwIHY2dkRHh5O+/bt+eqrr3j11VeJjY2ldevW5M2bl+HDh5M3b14WLlxIu3btWLlyJaVLlzbaCgsLY+jQodjZ2eHm5pZhnCNGjODLL7/ko48+olq1ahw6dIgZM2bwxx9/MGfOHHr16sUrr7xiTEl6/fXXH+t8HD9+nJYtW+Ls7MywYcOwsrJi0aJFdOzYkXnz5lGjRo10dcaOHcv69euZMWMGderUAeCrr75i0KBB+Pr60r9/f86ePcvUqVM5fvw48+fPx8LCAoBLly4xevRoevbsSfHixZk7dy7+/v5UqlSJ0qVLs379eiZPnoy/vz8uLi6cPHmSiRMncvPmTSZOnPhYYxSRp0sJhIiI5CgODg7GBWdWTGW6ceMGoaGhxgV9VFQUy5YtY8GCBXh6egIQExPDxIkTuX79Ovnz5wfuPhGYMWMGrq6uALi5udGgQQMWL16Mv78/Cxcu5OrVqyxdupTixYsDULduXXx8fJg2bRrTp083YmjUqBH//e9/Hxjj8ePHWblyJQMHDqRbt24A1K5dmyJFiuDn58e2bduoV68e//rXv4Anm5IUHh6OtbU1ixYtws7ODri7dqJJkyZMmjSJlStXmpQPCQkhMjKS8PBw6tatC9x9IhEcHMybb75JcHCwUdbZ2ZlOnTrx448/4uXlBcDNmzcZN26cca6dnZ2pX78+P/74I6VLlyYqKorXXnuNdu3akStXLmrUqIGtrS3Xrl17rPGJyNOnKUwiIpLjeHt78+677zJnzhx+//33J2rLwcHB5GmAk5MTgMmTgAIFCgBw/fp1Y1+JEiWM5AGgcOHCVK5cmV9++QWAXbt2Ub58eYoWLUpycjLJycnkypWLunXr8tNPP5nEUL58+YfGGBUVBUDjxo1N9jdu3BhLS0t2795t7nAfKSoqivr16xvJA0Du3Llp3LgxBw8e5MaNG8b+zz//nNmzZ9O4cWMjIYC7ayvOnz+Pt7e3Mfbk5GSqV6+OnZ0dO3fuNOmzcuXKxr9feeUVAGOqU82aNTl16hTNmzcnPDycAwcO4Ovr+0RTtETk6dITCBERyZGGDRvGrl27jKlMj+veC+V72draPrReWqJxr0KFCnHu3Dng7oLmmJgY/v3vf2dY/+bNm2b3lXa3/f43JeXOnZuCBQsSFxf30PqZce3atQzH5uTkRGpqqsnag8OHD1OnTh3Wr19Px44djbc+pS3mHjVqFKNGjUrX1sWLF0228+bNa/w7V6679y5TU1MB8PHx4c6dO3zxxRdEREQQFhZG8eLFGTRoED4+Pk82WBF5KpRAiIhIjuTg4EBQUBC9e/cmIiIiwzIpKSkm2/cu4H1SGU2huXTpkvGWI3t7e2rUqIGfn1+G9TOzdsPBwcFoP206FEBSUhJXrlyhYMGCmQn9kX1dvnw53f5Lly4BULBgQSMB+Pjjj+nQoQONGzdm2LBhrFixAktLS2Oal5+fX4ZrJtLGY64mTZrQpEkT4uLi2LFjB5999hmDBw+matWqFC1aNLNDFJGnTFOYREQkx2rQoAFNmjRh9uzZxMbGmhyzs7PjwoULJvv27t2bZX2fOnWKv/76y9g+d+4c+/btw8PDA4AaNWpw6tQpXn/9dSpVqmT8fPnll6xcuRJLS0uz+0q7CL93EXfadkpKClWrVs2CEd1VvXp1fvjhB5MnDSkpKWzYsIFKlSqZJD5OTk7Y2NgwYsQIfv/9d+bPnw9AqVKlKFSoEGfOnDEZe9GiRQkJCUn3tqqH6d+/P7179wbuJmWNGjWiV69eJCcnp3uSISI5g55AiIhIjjZ8+HB+/vnndHfNvby82LBhA25ubpQsWZLVq1cTExOTZf3myZOHnj17MmDAAFJSUpg2bRoFChSgY8eOAHTq1Ikvv/ySTp060aVLFwoWLMjXX3/N8uXLCQwMzFRfZcqU4b333mP69OncvHmT6tWr88cffxAeHo6Hhwdvvvlmlo2rT58+bNu2jQ4dOtCtWzesrKxYsmQJp0+fZs6cORnWqVevHu+88w5hYWE0bNiQEiVKMGDAAEaMGIGlpSX169fn+vXrREREcOHChQdO68pIzZo1GTlyJBMnTqRu3bpcv36d8PBwnJ2deeONN7Jq2CKShZRAiIhIjlagQAGCgoLo06ePyf7AwECSk5OZOHEiuXPnxsfHh4EDBzJs2LAs6bdChQo0bNiQoKAg4uLi8PT0ZMiQIcYUpqJFi7Js2TJCQkIICgri9u3bODs7M27cON5///1M9zdu3DhKlizJqlWr+OyzzyhSpAgdOnSgV69exrqBrFC2bFm++OILpkyZQmBgIBYWFri6urJo0SKqVav2wHpDhgxhx44dDB8+nAULFtCiRQvy5cvHnDlziIyMxNbWlipVqhAcHJzhl9w9SOvWrUlKSmLZsmV88cUX2NjY4OnpyeDBg7GyssqKIYtIFrNITVvFJCIiIiIi8ghaAyEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImZTAiEiIiIiImb7f6i9V5LTNrJ2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot of number of tokens \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 2.5))\n",
    "\n",
    "usage_df['gpt-3.5-prompt-tokens'] = usage_df['gpt-3.5-prompt-tokens'] + usage_df['gpt-3.5-16k-prompt-tokens']\n",
    "usage_df['gpt-3.5-completion-tokens'] = usage_df['gpt-3.5-completion-tokens'] + usage_df['gpt-3.5-16k-completion-tokens']\n",
    "\n",
    "# horizontal box plot (log scale)\n",
    "# without flier\n",
    "sns.boxplot(data=usage_df[['gpt-3.5-prompt-tokens', 'gpt-4-prompt-tokens', 'gpt-3.5-completion-tokens', 'gpt-4-completion_tokens']], orient=\"h\", palette=\"Set2\", ax=ax, flierprops=dict(markerfacecolor='0.50', markersize=0, linestyle='none'))\n",
    "plt.xscale('log')\n",
    "ax.set_xlabel('Number of Tokens')\n",
    "plt.title('Number of Tokens for Model Input/Output')\n",
    "plt.tight_layout()\n",
    "# save \n",
    "plt.savefig('./figures/RQ4_tokens.pdf')"
   ]
  }
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